class_docstr: torchaudio.models.RNNT()
  
  Recurrent neural network transducer (RNN-T) model.
  
  Note:
      To build the model, please use one of the factory functions.
  
  Args:
      transcriber (torch.nn.Module): transcription network.
      predictor (torch.nn.Module): prediction network.
      joiner (torch.nn.Module): joint network.
      attndim (int): TCPGen attention dimension
      biasing (bool): If true, use biasing, otherwise use standard RNN-T
      deepbiasing (bool): If true, use deep biasing by extracting the biasing vector
      embdim (int): dimension of symbol embeddings
      jointdim (int): dimension of the joint network joint dimension
      charlist (list): The list of word piece tokens in the same order as the output layer
      encoutdim (int): dimension of the encoder output vectors
      dropout_tcpgen (float): dropout rate for TCPGen
      tcpsche (int): The epoch at which TCPGen starts to train
      DBaverage (bool): If true, instead of TCPGen, use DBRNNT for biasing
docstr: Applies joint network to source and target encodings.
  
  B: batch size;
  T: maximum source sequence length in batch;
  U: maximum target sequence length in batch;
  D: dimension of each source and target sequence encoding.
  A: TCPGen attention dimension
  
  Args:
      source_encodings (torch.Tensor): source encoding sequences, with
          shape `(B, T, D)`.
      source_lengths (torch.Tensor): with shape `(B,)` and i-th element representing
          valid sequence length of i-th batch element in ``source_encodings``.
      target_encodings (torch.Tensor): target encoding sequences, with shape `(B, U, D)`.
      target_lengths (torch.Tensor): with shape `(B,)` and i-th element representing
          valid sequence length of i-th batch element in ``target_encodings``.
      hptr (torch.Tensor): deep biasing vector with shape `(B, T, U, A)`.
  
  Returns:
      (torch.Tensor, torch.Tensor, torch.Tensor):
          torch.Tensor
              joint network output, with shape `(B, T, U, output_dim)`.
          torch.Tensor
              output source lengths, with shape `(B,)` and i-th element representing
              number of valid elements along dim 1 for i-th batch element in joint network output.
          torch.Tensor
              joint network second last layer output, with shape `(B, T, U, D)`.
docstr_tok: ["Applies","joint","network","to","source","and","target","encodings",".","B",":","batch","size",";","T",":","maximum","source","sequence","length","in","batch",";","U",":","maximum","target","sequence","length","in","batch",";","D",":","dimension","of","each","source","and","target","sequence","encoding",".","A",":","TCPGen","attention","dimension","Args",":","source_encodings","(","torch.Tensor",")",":","source","encoding","sequences",",","with","shape","`","(","B",",","T",",","D",")","`",".","source_lengths","(","torch.Tensor",")",":","with","shape","`","(","B",",",")","`","and","i-th","element","representing","valid","sequence","length","of","i-th","batch","element","in","``","source_encodings","``",".","target_encodings","(","torch.Tensor",")",":","target","encoding","sequences",",","with","shape","`","(","B",",","U",",","D",")","`",".","target_lengths","(","torch.Tensor",")",":","with","shape","`","(","B",",",")","`","and","i-th","element","representing","valid","sequence","length","of","i-th","batch","element","in","``","target_encodings","``",".","hptr","(","torch.Tensor",")",":","deep","biasing","vector","with","shape","`","(","B",",","T",",","U",",","A",")","`",".","Returns",":","(","torch.Tensor",",","torch.Tensor",",","torch.Tensor",")",":","torch.Tensor","joint","network","output",",","with","shape","`","(","B",",","T",",","U",",","output_dim",")","`",".","torch.Tensor","output","source","lengths",",","with","shape","`","(","B",",",")","`","and","i-th","element","representing","number","of","valid","elements","along","dim","1","for","i-th","batch","element","in","joint","network","output",".","torch.Tensor","joint","network","second","last","layer","output",",","with","shape","`","(","B",",","T",",","U",",","D",")","`","."]
code: def join(
      self,
      source_encodings: torch.Tensor,
      source_lengths: torch.Tensor,
      target_encodings: torch.Tensor,
      target_lengths: torch.Tensor,
      hptr: torch.Tensor = None,
  ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
      r"""Applies joint network to source and target encodings.
  
      B: batch size;
      T: maximum source sequence length in batch;
      U: maximum target sequence length in batch;
      D: dimension of each source and target sequence encoding.
      A: TCPGen attention dimension
  
      Args:
          source_encodings (torch.Tensor): source encoding sequences, with
              shape `(B, T, D)`.
          source_lengths (torch.Tensor): with shape `(B,)` and i-th element representing
              valid sequence length of i-th batch element in ``source_encodings``.
          target_encodings (torch.Tensor): target encoding sequences, with shape `(B, U, D)`.
          target_lengths (torch.Tensor): with shape `(B,)` and i-th element representing
              valid sequence length of i-th batch element in ``target_encodings``.
          hptr (torch.Tensor): deep biasing vector with shape `(B, T, U, A)`.
  
      Returns:
          (torch.Tensor, torch.Tensor, torch.Tensor):
              torch.Tensor
                  joint network output, with shape `(B, T, U, output_dim)`.
              torch.Tensor
                  output source lengths, with shape `(B,)` and i-th element representing
                  number of valid elements along dim 1 for i-th batch element in joint network output.
              torch.Tensor
                  joint network second last layer output, with shape `(B, T, U, D)`.
      """
      (
          output,
          source_lengths,
          target_lengths,
          jointer_activation,
      ) = self.joiner(
          source_encodings=source_encodings,
          source_lengths=source_lengths,
          target_encodings=target_encodings,
          target_lengths=target_lengths,
          hptr=hptr,
      )
      return output, source_lengths, jointer_activation
  
imports: import math
  from typing import Dict, List, Optional, Tuple
  import torch
  from torchaudio.models import Conformer, RNNT
  from torchaudio.models.rnnt import _Joiner, _Predictor, _TimeReduction, _Transcriber
label_desc: Use image node_id 8 for calling the RNNTBiasing obj's underlying member method code with example usage: obj.join(source_encodings, source_lengths, target_encodings, target_lengths, hptr) and returns: output,  source_lengths,  jointer_activation